Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

• Main Authors: Jia Liu (Author), ShuYang Dai (Author), Gong Chen (Author), Song Sun (Author), JingYing Jiang (Author), Shan Zheng (Author), YiJie Zheng (Author), Rui Dong (Author)
• Format: Book
• Published: Frontiers Media S.A., 2020-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN).Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness.Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%.Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.

Item Description

2296-2360
10.3389/fped.2020.00409